🌍 Circadian Timing of Radiotherapy and Survival Outcomes

📌 Background & Rationale

Radiotherapy has "optimized" the "where" and "how much" of dose delivery, yet the "when"—the optimal time of day—remains a largely unexploited variable in precision medicine. Driven by circadian rhythms, the efficacy of ionizing radiation fluctuates based on the timing of DNA repair mechanisms and cell cycle phases. Theoretically, targeting specific circadian windows could improve outcomes, yet clinical data remain contradictory and scarce.

🔬 Study Design & The "Natural Experiment"

Unlike typical radiotherapy centers that operate only during daylight hours, our institution’s resource-constrained 22-hour operational model (07:00 to 05:00 the following day) provided an unprecedented 'natural experiment' opportunity. This allowed us to capture data from the rarely studied nocturnal window (00:00–06:00), filling a critical knowledge gap in chronobiology.

By utilizing a massive dataset derived directly from major Oncology Information Systems (ARIA and MOSAIQ), this study analyzed objective treatment timestamps from 86,433 patients treated between 2010 and 2025, encompassing 13 major cancer types such as nasopharyngeal carcinoma (NPC), lung cancer, and esophageal cancer. This approach effectively eliminates recall bias and provides the most comprehensive evaluation to date.

💡 Key Findings

Our analysis reveals significant heterogeneity in the survival impact of radiotherapy timing across tumor types:

🌙 Night treatment (00:00–06:00) significantly improved survival in nasopharyngeal (HR 0.65) and esophageal (HR 0.86) cancers.
☀️ Conversely, it increased mortality risk in lung cancer (HR 1.24).

📊 Supplementary Data Navigation

  • [Table 1] Baseline Characteristics of the cohort.
  • [Table 2] Interaction Effects between tumor type and circadian timing.
  • [Figure 1B-C] RT Time Distribution: Polar plots at patient and fraction levels.
  • [Figure 2] Forest Plots: Main multivariable Cox regression visualization.
  • [Figure 3] Non-linear Trends (RCS): Restricted Cubic Splines modeling.
  • [eTable 1-6] Baseline Characteristics for six major tumor types.
  • [eTable 7-8] Seasonality Analysis: Assessing the impact of treatment season.
  • [eTable 9] Interaction Effects: Tumor type × Circadian timing interactions.
  • [eTable 10-15] Comprehensive Cox Data: Detailed numerical results for Figure 2.
  • [eTable 16-21] PSM Validation: Propensity Score Matching results.
  • [eTable 22-24] Core Tumor Sensitivity: Robustness checks and E-values.
  • [eFigure 1] Timing Deviation Distribution.
  • [eFigure 2-3] KM Survival Curves: Kaplan-Meier estimates across groups.
  • [eFigure 4-6] PSM Validation: Propensity Score Matching results.
  • [eFigure 7-9] Subgroup Analysis: Consistency across age, sex, and stage.
  • [eFigure 10] Sensitivity analysis for 10 years follow-up.
  • [eFigure 11-12] Timing Deviation: Sensitivity analysis excluding Δt > 2h.